Key result
Automated ensemble framework accurately predicts lifestyle-related diseases using real-world clinical data.
Why the study?
Most existing research focuses on the prognosis of a single lifestyle-related disease, limiting the ability to intelligently identify important disease characteristics.
A novel machine learning framework demonstrates good accuracy in predicting lifestyle-related diseases using hospital data.
With the improvement of living standards and changes in work habits caused by industrialization, the prevalence of diseases related to lifestyle is rising. In this context, the prevention of lifestyle-related diseases (LRDs) is extremely important. The majority of existing research exclusively concentrates on the prognosis of a particular LRD sickness, making it impossible for them to intelligently identify the important characteristics of the disease. Therefore, this study aims to propose a lifestyle-related disease prediction framework including three key components, called missing value module, feature selection module, and disease prediction module. The performance of the proposed framework is evaluated by using real medical data gathered during a hospital in Nanjing, China. The experiment shows that the proposed framework can automatically generate prediction ensemble models for specific LRDs diseases, and achieve good accurate performance.
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Ren et al. (2023) studied Lifestyle-related diseases. Lifestyle-related disease prediction framework was evaluated on Prediction accuracy/performance. A proposed lifestyle-related disease prediction framework automatically generated prediction ensemble models and achieved good accurate performance using real medical data from a hospital in China.
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